Here are three major results obtained so far in this research project:[unreadable] [unreadable] 1. For over two decades the National Toxicology Program (NTP) has been interested in developing a formal statistical procedure for analyzing data from their 2-year rodent cancer bioassay that incorporates their historical control data base. Several statistical procedures have been proposed over the past two decades but the Technical Reports Subcommittee of the NTP Board of Scientific counselors has not endorsed any of the existing methods and recommended that a new procedure be developed for this important problem. In response to this requirement, we developed a new statistical procedure for this important long standing problem. The resulting paper was published in the Journal of American Statistical Association in 2007. We are currently evaluating the performance of this procedure so that it can be used for analyzing future NTP bioassays. [unreadable] [unreadable] 2. Often resampling procedures, such as bootstrap methodology, are used for analyzing microarray gene expression data. However, as the number of genes on a microarray chip increases, the computation burden using resampling procedures increases significantly. In our research program we have developed a simple adaptive bootstrap methodology that reduces the computation burden substantially without impacting the false discovery rate. This work was published in "Statistical Applications in Genetics and Molecular Biology."[unreadable] [unreadable] 3. Often researchers at NIEHS (and at other places) collect data from dose-response, time-course experiments where the outcome variable is ordinal. In such cases the existing methods of analysis are not satisfactory, as they can be severely under-powered. Motivated by a data set obtained received in our consulting service, we developed a new general methodology which is applicable to a very broad collection of data sets including ordinal data and two-dimensional gene expression microarray data. This work was published as an invited chapter in "IMS Collections. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen."